---
library_name: transformers
license: mit
base_model: AnonymousCS/populism_multilingual_roberta_base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: populism_model58
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# populism_model58

This model is a fine-tuned version of [AnonymousCS/populism_multilingual_roberta_base](https://huggingface.co/AnonymousCS/populism_multilingual_roberta_base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3075
- Accuracy: 0.9258
- F1: 0.5778
- Recall: 0.7879
- Precision: 0.4561

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| No log        | 1.0   | 16   | 0.2858          | 0.8320   | 0.4267 | 0.9697 | 0.2735    |
| No log        | 2.0   | 32   | 0.2872          | 0.9219   | 0.5745 | 0.8182 | 0.4426    |
| No log        | 3.0   | 48   | 0.3606          | 0.9336   | 0.5278 | 0.5758 | 0.4872    |
| 0.2954        | 4.0   | 64   | 0.2670          | 0.9004   | 0.5234 | 0.8485 | 0.3784    |
| 0.2954        | 5.0   | 80   | 0.3075          | 0.9258   | 0.5778 | 0.7879 | 0.4561    |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0